In this study, we describe the derivation and evaluation of Top of the Atmosphere (TOA) Shortwave Radiative (SWR) Fluxes from the Advanced Baseline Imager (ABI) sensor on the GOES-18 satellite. The TOA estimates use narrowband observations from ABI that are transformed to broadband (NTB), based on simulations and adjusted to total fluxes using Angular Distribution Models (ADMs). Subsequently, the GOES-18 estimates are evaluated against the Clouds and the Earth’s Radiant Energy System (CERES) data, the only observed SWR broadband flux dataset. The importance of agreement at the TOA is that most methodologies to derive surface SWR start with the satellite observation at the TOA. Moreover, information needed to compute radiative fluxes at both boundaries (TOA and surface) is needed for estimating the energy absorbed by the atmosphere. The methodology described was comprehensively evaluated, and possible sources of errors were identified. The results of the evaluation for the four seasonal months indicate that by using the best available auxiliary data, the accuracy achieved in estimating TOA SWR at the instantaneous scale ranges between 0.55 and 17.14 W m−2 for the bias and 22.21 to 30.64 W m−2 for the standard deviation of biases (differences are ABI minus CERES). It is believed that the high bias of 17.14 for July is related to the predominantly cloudless sky conditions, when the used ADMs do not perform as well as for cloudy conditions.

The Aralkum Desert presents a challenging environment for satellite aerosol observations due to its very bright, heterogeneous, and dynamic surfaces and the lack of in situ constraints on region-specific aerosol properties. We survey current global satellite algorithms capable of detecting the presence, column burden, and elevation of airborne dust over the Aral Sea basin. Discrepancies and potential biases in retrieved UV aerosol index (UVAI), mid-visible and thermal infrared optical depth (AOD), and layer height due to different assumptions on surface and aerosol properties are assessed. The results indicate that (1) UVAI products consistently delineate dust plume extent but show large positive values over turbid waters and salt flats due to enhanced surface absorption. (2) MODIS and VIIRS total and coarse-mode AOD retrievals show strong agreement over the Caspian Sea despite using different aerosol optical models. Over desert surfaces, all operational AOD products misclassify fresh dust plumes as clouds and exhibit strong nonlinear relationships. The NOAA EPS algorithm retrieves significantly lower AOD than others, although the agreement improves when a dust optical model is used. The MISR research algorithm produces higher, more consistent AOD and improved particle property retrievals compared to the MISR operational product. (3) Among four IASI infrared products, the LMD algorithm performs best in detecting dust plume features over both desert and water surfaces. (4) The EPIC aerosol optical centroid height (AOCH) product overestimates dust layer altitude under low aerosol loadings but exhibits good agreement with CALIOP in detecting the elevated dust characterized by well-defined upper boundaries. MISR height retrievals also align well with CALIOP and EPIC. IASI infrared retrievals are about 0.4 km higher than EPIC over dust-laden scenes. This study underscores the value of a synergistic, multisensor approach leveraging the complementary strengths of satellite aerosol products and calls for their appropriate application and careful interpretation when characterizing saline dust from the Aralkum Desert.

Background/Objectives: The most important prognostic factors in curatively treated prostate cancer are T and N stage, histology, grade group and initial PSA. A recent study found that men with blood calcium levels at the high end of the normal range are over two-and-a-half times more likely to develop fatal prostate cancer than those with lower calcium levels. However, there is limited evidence regarding the prognostic value of calcium levels at the time of prostate cancer diagnosis. We aimed to determine whether a calcium level in the upper range of normal values has any prognostic value in curatively treated prostate cancer. Methods: We conducted a retrospective analysis of 84 consecutive patients with prostate cancer who underwent curative-intent radiotherapy—either as primary treatment or adjuvant therapy—using external beam radiotherapy with or without brachytherapy. We analyzed all pertinent prognostic factors that could potentially impact disease-free survival. Results: The study revealed that calcium levels at diagnosis significantly predict disease-free survival, whereas the initial PSA level did not hold prognostic significance—likely due to interference from benign prostatic hyperplasia. Conclusions: If our findings are validated, calcium levels at the time of prostate cancer diagnosis could be incorporated into future predictive and prognostic models.

Aerosol optical depth (AOD) is a vital parameter in atmospheric research. Using observations of the Visible Infrared Imaging Radiometer Suite (VIIRS), onboard Suomi National Polar-orbiting Partnership (Suomi-NPP) and NOAA-20 satellites, National Oceanic and Atmospheric Administration (NOAA) produces near-real time AOD product with high pixel resolution (750 m), wide swath width (3,040 km), and a 16-day repeat cycle. Here we report the evaluation of the NOAA/VIIRS AOD using a comprehensive aerosol data set, derived from a global-scale, multi-seasonal airborne mission, the NASA Atmospheric Tomography Mission (ATom). This data set includes rich physical and chemical information, such as size distributions, chemical compositions, optical properties, and hygroscopicities of major aerosol types, including dust, sea salt, smoke, internally mixed sulfate/nitrate/organics particles (non-smoke), black carbon, etc. Globally, VIIRS AOD (Suomi-NPP and NOAA-20) shows good agreement with the ATom AOD in the moderate to high AOD range (>0.3), with respect to measurement uncertainties (orthogonal distance regression fitting slope: 1.5 +/- 0.2 for Suomi-NPP and 1.6 +/- 0.5 for NOAA-20; correlation coefficient: 0.85 for Suomi-NPP and 0.73 for NOAA-20). There is a persistent bias in the low AOD range (<0.3) on the order of 0.03, likely reflecting systematic errors on VIIRS and/or the ATom AOD product. & Aring;ngstr & ouml;m exponent reported by VIIRS shows excellent agreement with ATom results within expected uncertainties. Given the unique insights revealed by the ATom AOD and aerosol property data set, it is desirable to have ATom-like comprehensive payloads in future airborne satellite validation programs.

Abstract. The performance and consistency of satellite observations in characterizing the saline dust emission from the newly formed Aralkum Desert have remained poorly understood. We address this knowledge gap by providing a review of satellite techniques capable of detecting the presence, column burden, and vertical height of airborne dust over desert surfaces. Then we evaluate the consistency between different aerosol products in observing an intense Aralkum dust storm in 2018, via synergistic analyses of the ultraviolet aerosol index (UVAI) from OMPS, TROPOMI and EPIC, aerosol optical depth (AOD) from MODIS and VIIRS, and aerosol optical centroid height (AOCH) from CALIOP and EPIC. The UVAI products consistently delineate the areal extent of the freshly emitted dust plume if the dynamic range of each product is considered. The heavy dust plume is however erroneously masked as clouds in the AOD products. All UVAI products show large positive values over the Garabogazköl gulf and northern Caspian Sea due to enhanced UV absorption by turbid and saline waters, suggesting that caution must be taken to avoid misinterpreting the surface effect as dust signal over ephemeral or dried lakes. The AOD products show generally good agreement in observing the total and coarse-mode AOD associated with the dust outflow to Caspian Sea. Over-land AOD retrievals show strong non-linear relationships between aerosol algorithms. The NOAA Enterprise Processing System (EPS) product yields significantly lower AOD than other algorithms, likely due to the misuse of an urban aerosol optical model for dust retrieval. The EPIC AOCH retrieval shows the best agreement with CALIOP over heavy dust burden areas, with both mean bias and RMSE below 0.5 km. This study reveals significant inconsistency between satellite aerosol products and the potential of multi-sensor approaches for identifying the product biases and limitations in Central Asia.

The most important prognostic factors in curatively treated prostate cancer at the initial diagnosis are T and N stage, histological subtype, Gleason score, initial PSA, PSA density and percentage of core biopsy positivity. Newer, but non-standard prognostic factors are urine PCA3 and genetic markers of prostate cancer. A simple, but seemingly underestimated standard test is serum Calcium (Ca) level, which has been shown by a previous study group to be a good predictor of lethal prostate cancer, but there is scarce evidence regarding the prognostic value of calcium levels at diagnosis. We aimed to determine whether a Ca level in the upper part of normal values has a prognostic value in curatively treated prostate cancer. We have retrospectively included 84 consecutive patients with prostate cancer, who underwent curative radiotherapy (primary or adjuvant; external beam RT with or without brachytherapy). We have performed statistical analysis of available prognostic factors. Ca level at diagnosis was found to be a statistically significant predictor of disease-free survival.

Abstract. Global monitoring of aerosols is required to analyse the impacts of aerosols on air quality and to understand their role in modulating the climate variability. The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecasts and reanalyses of aerosols using the ECMWF Integrated Forecasting System (IFS), constrained by the assimilation of MODIS and Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). Given the potential end of lifetime of MODIS AOD, implementing new AOD observations in the CAMS operational suite is a priority to ensure the continuity of the CAMS forecast performances. The objective of this work is to test the assimilation of the NOAA VIIRS AOD product from S-NPP and NOAA20 satellites in the IFS model. Simulation experiments assimilating VIIRS on top or in place of MODIS were carried out from June 2021 to November 2021 to evaluate the impacts on the AOD analysis. For maritime aerosol background, the assimilation of VIIRS and the use of VIIRS from NOAA20 as an anchor reduce the analysis AOD values compared to MODIS-based experiments, in which the analysis values were too high due to the positive bias of MODIS/Terra over ocean. Over land, the assimilation of VIIRS induces a large increase in the analysis over biomass burning regions where VIIRS shows larger AOD than MODIS due to differences in the aerosol models and cloud filtering between MODIS and VIIRS retrieval algorithms. For dust source regions, the analysis is reduced when VIIRS is assimilated on top of or in place of MODIS, particularly over the Sahara, the Arabian Peninsula and a few places in Asia in the July–August period. The assimilation of VIIRS leads to an overall reduction of the bias in AOD analysis evaluated against AERONET measurements, with the largest bias reduction over Europe and desert and maritime sites.

This study investigated the impact of COVID-19 lockdowns on satellite aerosol optical depth (AOD), to explore the hypothesis that if changes in economic activity are seen in emissions of NO 2 , an aerosol precursor, then AOD should change commensurably. We developed a technique to filter AOD data to isolate changes associated with anthropogenic emissions. Overall, in 37 of the 43 cities that were identified as top oxides of nitrogen (NO x ) emitters from their transportation sectors, AODs decreased by 21.2% ± 7.8%, 18.9% ± 11.7%, 27% ± 12.4%, 22.9% ± 7.6% in the United States, India, western Europe, and China, respectively—an average of 22.4% ± 7.4%. In contrast, AODs increased on average by 11.7% ± 8.4% in Taiwan, where economic stimulus was used as a strategy during the pandemic. This analysis implies NO x and volatile organic compounds emissions reductions from the transportation sector can be targeted, and by transitioning 6 million light duty vehicles from gasoline to electricity, the US can achieve 21% improvement in AOD.

Under the GOES-R activity, new algorithms are being developed at the National Oceanic and Atmospheric Administration (NOAA)/Center for Satellite Applications and Research (STAR) to derive surface and top-of-the-atmosphere (TOA) shortwave (SW) radiative fluxes from the Advanced Baseline Imager (ABI), the primary instrument on GOES-R. This paper describes a support effort in the development and evaluation of the ABI instrument capabilities to derive such fluxes. Specifically, scene-dependent narrow-to-broadband (NTB) transformations are developed to facilitate the use of observations from ABI at the TOA. Simulations of NTB transformations have been performed with MODTRAN 4.3 using an updated selection of atmospheric profiles and implemented with the final ABI specifications. These are combined with angular distribution models (ADMs), which are a synergy of ADMs from the Clouds and the Earth's Radiant Energy System (CERES) and from simulations. Surface conditions at the scale of the ABI products as needed to compute the TOA radiative fluxes come from the International Geosphere–Biosphere Programme (IGBP). Land classifications at 1/6∘ resolution for 18 surface types are converted to the ABI 2 km grid over the contiguous United States (CONUS) and subsequently re-grouped to 12 IGBP types to match the classification of the CERES ADMs. In the simulations, default information on aerosols and clouds is based on that used in MODTRAN. Comparison of derived fluxes at the TOA is made with those from CERES, and the level of agreement for both clear and cloudy conditions is documented. Possible reasons for differences are discussed. The product is archived and can be downloaded from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).

We present an overview and several important upgrades to the Vector Discrete Ordinate Radiative Transfer (VDISORT) code. VDISORT is a polarized (vector) radiative transfer code that can be applied to a wide range of research problems including the Earth’s atmosphere and ocean system. First, a solution is developed to the complex algebraic eigenvalue problem resulting when the b 2 component of the Stokes scattering matrix is non-zero. This solution is needed to compute the V component of the Stokes vector I=[I∥,I⊥,U,V]T . Second, a significant improvement in computational efficiency is obtained by reducing the dimension of the algebraic eigenvalue by a factor of 2 resulting in a speed increase of about 2 3 = 8. Third, an important upgrade of the VDISORT code is obtained by developing and implementing a method to enable output at arbitrary polar angles by the integration of the source function (ISF) method for partially reflecting Lambertian as well as general non-Lambertian surfaces. Fourth, a pseudo-spherical treatment has been implemented to provide important corrections for Earth curvature effects at near horizontal solar zenith and observation (viewing) polar angles. Fifth, a post-processing single-scattering correction procedure has been developed to enhance the accuracy and speed for strongly forward-peaked scattering. With these significant improvements the results from the upgraded version of the VDISORT code match published benchmark results for Rayleigh scattering, Mie scattering, and scattering by non-spherical cirrus particles. The performance of VDISORT for a polarized incident beam source is equally satisfactory. The VDISORT vector radiative transfer code is made public and freely available for use by the growing polarimetric research community including the space-borne polarimeters on the future NASA PACE and AOS missions.

The National Oceanic and Atmospheric Administration (NOAA) and its international partners have begun to deploy a new generation of sensors into geostationary orbit. NOAA's Geostationary Operational Environmental Satellites (GOES)-R and GOES-S (now called GOES-16 and GOES-17) carry Advanced Baseline Imager (ABI) instruments for imaging Earth's weather, oceans, and atmosphere. NOAA began the production of ABI aerosol products, which include aerosol optical depth (AOD), aerosol detection (dust/smoke mask) and imagery [GeoColor and dust red-green-blue (RGB)] products. The AOD algorithm is based on a dark target approach and the aerosol detection is based on spectral differencing techniques. From analysis of the prelaunch algorithm testing, the expected accuracy for AOD is dependent on aerosol amounts and whether the surface is land (expected accuracies of 0.06, 0.04, 0.12 for low, medium, high AODs, respectively) or water (expected accuracies of 0.02 and 0.1 for low and medium AODs, respectively), where for example, accuracy of 0.06 implies retrieved AOD is within ± 0.06 of truth. For aerosol detection, the expected accuracy is 80% except for smoke over the ocean where it is 70%. NOAA plans to expand the coverage to bright surfaces based on the Aerosol Enterprise Processing System (EPS) algorithm that was developed and operationally implemented for the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS). This chapter reviews the history of geostationary satellite aerosol retrievals, highlights the new GOES-16 ABI aerosol algorithms and products, and discusses how the long-range transport of smoke from fire events in Canada on August 16, 2018, for example, affected the air quality in the Mid-Atlantic states in the US.

The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multiband algorithm similar to those of polar-orbiting satellites' sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to limitations in the land surface reflectance relationships between the 0.47 µm band and the 2.2 µm band and between the 0.64 µm band and 2.2 µm band used in the ABI AOD retrieval algorithm, which vary with the Sun–satellite geometry and NDVI (normalized difference vegetation index). To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30 d period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period 6 August to 31 December 2018 are used to evaluate the bias correction algorithm. After bias correction, the correlation between the top 2 quality ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root-mean-square error (RMSE) improves from 0.09 to 0.05. These results for the bias-corrected top 2 qualities ABI AOD are comparable to those of the corrected high-quality ABI AOD. By using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the areal coverage of ABI AOD is increased by about 100 % without loss of data accuracy.

For the past two decades, quantitative retrievals of aerosol optical depth (AOD) have been made from both geostationary and polar-orbiting satellites, and the results have been widely used in numerous studies. Despite the progress made in improving the accuracy of AOD retrievals, there are still major challenges, especially over land. A notable one for the so-called Dark-Target (DT) algorithms is building the surface reflectance (SR) relationships (SRR) to derive SR in the visible channels from SR in the short-wave infrared (SWIR) channel, mainly because these relationships are strongly subjected to entangled factors (e.g., viewing geometry, surface type, and vegetation state). In this study, we examine the benefits of a new method for deriving the SRR using deep learning techniques. The SRR constructed by the deep neural network (DNN) considers multiple related inputs, such as the SWIR normalized difference vegetation index (NDVISWIR), viewing geometry, and seasonality, among others. We then incorporate the DNN-constrained SRR into a DT algorithm developed at NOAA/STAR to retrieve AOD from the Advanced Himawari Instrument (AHI) onboard the new generation of geostationary satellites, Himawari-8. The revised DT algorithm with the deep learning technique (DTDL) demonstrates improved performance over the study region (95–125°E, 18–30°N, a portion of the AHI full disk), as attested by significantly reduced random noise, especially for low NDVISWIR and high surface albedo cases. Robust independent tests indicate that this algorithm can be applied to untrained regions, not only to those used in training. The method directly benefits the algorithm development for Himawari-8 and can also be adopted for other geostationary or polar-orbiting satellites. Our study illustrates how artificial intelligence could significantly improve AOD retrievals from multi-spectral satellite observations following this new approach.

Two components of SRB, solar radiation reflected to space and solar radiation reaching the surface, are retrieved from the Advanced Baseline Imager (ABI). Physical algorithms are used that combine forward and inverse methods to estimate reflection and transmission and account for all major interactions of the radiation with the atmosphere and the surface. Owing to the improved upstream ABI cloud and aerosol product inputs, and because of availability of calibrated solar reflective bands on ABI, the two products represent an improvement in quality over legacy radiation products. Preliminary evaluation of the two products with reference data indicates that they meet expectations.

Imaging radiometers such as the advanced baseline imager and the advanced Himawari imager on the new generation geostationary environmental satellites provide new opportunities for several non-meteorological applications. Examples of three such applications in fire detection, monitoring atmospheric aerosols for air quality and modelling downwelling solar radiation for power plants are presented here.

This paper compares three dust detection algorithms over land that were developed for operational, near-real-time processing using the Suomi National Polar Orbiting Partnership Visible Infrared Imaging Radiometer Suite instrument. The three algorithm approaches use different spectral bands, namely deep blue bands, infrared (IR)-visible bands, and IR bands, and are applied for dust observed over dark as well as bright surfaces. The evaluations are performed both using case studies and AERONET matchup data over western CONUS-Mexico region and North Africa-Arabian Peninsula region. The deep blue-based algorithm is found to have the most false detections and its detection performance depends on the Sun-satellite geometries. Simulation analysis shows that there are three causes of this problem: surface reflectance, air mass factors, and phase functions in different geometries. The algorithm based on IR-visible bands has much less false detection than the deep blue bands-based algorithm and has better true positive detection than the IR-based algorithm. The IR bands-based algorithm performs well in the case studies over CONUS-Mexico region, but it fails to detect most of the dust cases over North Africa-Arabian Peninsula region. The results suggest that the IR-visible algorithm is the most suitable for the dust detection of the three algorithms with a small modification. Because the IR-visible algorithm is not able to detect all the dust pixels, detections from the deep blue algorithm only and those from the IR-visible algorithm with relaxed criteria are also provided but are distinguished with a lower quality. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.